Daniel E. Hurtado

Cardiology and Cardiovascular Medicine · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Cardiology and Cardiovascular Medicine — 21 citations into it; #421 of 906 by trust here, #665 by citations
• Artificial Intelligence — 16 citations into it
• Statistics and Probability — 15 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Statistical and Nonlinear Physics — 10 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Biomedical Engineering — 5 citations into it; #6,796 of 10,734 by trust here, #9,794 by citations

Whom Daniel E. Hurtado trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. F. Sahli Costabal
2. Michael L. Stein
3. Paris Perdikaris
4. James Hensman
5. Gerhard A. Holzapfel
6. Loic Le Gratiet
7. Greg Ver Steeg
8. Carl E. Rasmussen
9. Ellen Kuhl

Who trusts Daniel E. Hurtado overall

1. F. Sahli Costabal

Papers from Daniel E. Hurtado's perspective

Ordered by how strongly the researchers Daniel E. Hurtado trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Understanding the difficulty of training deep feedforward neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Information Theory, Inference, and Learning Algorithms
9. TensorFlow: A system for large-scale machine learning
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Generating Purkinje networks in the human heart (2015) — 118 citations
• Physics-Informed Neural Networks for Cardiac Activation Mapping (2020)
• Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019) — 56 citations
• The importance of mechano-electrical feedback and inertia in cardiac electromechanics (2017) — 66 citations